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            An end-to-end platform for machine learning
            Install TensorFlow

            Get started with TensorFlow

              TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples.

              View tutorials
              import tensorflow as tf
              mnist = tf.keras.datasets.mnist
              
              (x_train, y_train),(x_test, y_test) = mnist.load_data()
              x_train, x_test = x_train / 255.0, x_test / 255.0
              
              model = tf.keras.models.Sequential([
                tf.keras.layers.Flatten(input_shape=(28, 28)),
                tf.keras.layers.Dense(128, activation='relu'),
                tf.keras.layers.Dropout(0.2),
                tf.keras.layers.Dense(10, activation='softmax')
              ])
              
              model.compile(optimizer='adam',
                loss='sparse_categorical_crossentropy',
                metrics=['accuracy'])
              
              model.fit(x_train, y_train, epochs=5)
              model.evaluate(x_test, y_test)
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              Explore the latest advancements in running models client-side with speakers from Chrome, MediaPipe, Intel, Hugging Face, Microsoft, LangChain, and more.

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                TensorFlow GNN
            Analyze relational data using graph neural networks

              GNNs can process complex relationships between objects, making them a powerful technique for traffic forecasting, medical discovery, and more.

              Learn about TF GNN
                TensorFlow Agents
            Build recommendation systems with reinforcement learning

              Learn how Spotify uses the TensorFlow ecosystem to design an extendable offline simulator and train RL Agents to generate playlists.

              Read the blog

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                Train and run models directly in the browser using JavaScript or Node.js.

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                Deploy ML on mobile and edge devices such as Android, iOS, Raspberry Pi, and Edge TPU.

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                Preprocess data and create input pipelines for ML models.

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                Create production ML pipelines and implement MLOps best practices.

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                Create ML models with TensorFlow's high-level API.

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                Find pre-trained models ready for fine-tuning and deployment.

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                Browse the collection of standard datasets for initial training and validation.

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                Visualize and track development of ML models.

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